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IEEE Conference on Decision and Control

作者： Roya Doshmanziari Roxanne R. Jackson Damiano Varagnolo Steffi Knorn Norwegian University of Science and Technology in Trondheim Norway Technische Universität Berlin Germany University of Padova Italy

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

We propose a novel dynamical model for describing muscular fatigue and cramping following repeated muscular contractions in the case of female Kegel exercising. The proposed compartmental model adds opportune parameters that capture fatiguing effects while exercising, and extends them by adding a compartment modeling cramping muscles. The extended model can capture temporal dynamics effecting both the baseline muscular pressure of the patients and the peak pressures exerted while exercising, which cannot be captured with the models previously presented in the literature. However, this modification incurs a loss of identifiability of the model. Thus, we devise an opportune ad-hoc learning approach to solve the identification problem in a computationally efficient way. Quantitatively, we are then able to show that the adaptation to the model, improves the predictive capabilities for data that exhibit a cramping effect. We note that, the variability of the cramping on a daily basis leads to great variability in the results.

关键词： data-driven modeling Adaptation models Sensitivity analysis Computational modeling Heuristic algorithms Predictive models Muscles

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data-driven Energy modeling of Machining Centers Through Automata Learning

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2025年 22卷 5769-5780页

作者： Lestingi, Livia Frigerio, Nicla Bersani, Marcello M. Matta, Andrea Rossi, Matteo Politecn Milan Dept Elect Informat & Bioengn I-20133 Milan Italy Politecn Milan Dept Mech Engn I-20156 Milan Italy

The paper addresses the problem of estimating the energy consumed by production resources in manufacturing so that alternative process designs can be compared in terms of energy expenditure. In particular, the proposed methodology focuses on Computer Numerical Controlled (CNC) machining centers. Classical approaches to energy modeling require high expertise and large development effort since, for example, data acquisition is resource-specific and must be repeated frequently to avoid obsolescence. An automated and flexible data-driven methodology is designed in this work. A data-driven method is employed to learn a hybrid and stochastic model of a CNC machining center's energetic behavior. The learned model is used to provide offline energy consumption estimates of simulated part-programs before the actual execution of the cutting. Numerical results show the performance of the proposed method on a set of case studies. The methodology is also applied to a real industrial application, including data collected during machine production.

关键词： Machining Energy consumption Task analysis Biological system modeling Predictive models Numerical models Training Energy modeling machining automata learning data-driven modeling

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Distributed computing for physics-based data-driven reduced modeling at scale: Application to a rotating detonation rocket engine

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COMPUTER PHYSICS COMMUNICATIONS 2025年 313卷

作者： Farcas, Ionut-Gabriel Gundevia, Rayomand P. Munipalli, Ramakanth Willcox, Karen E. Univ Texas Austin Oden Inst Computat Engn & Sci Austin TX 78712 USA Virginia Tech Dept Math Blacksburg VA USA Amentum Edwards AFB CA USA Air Force Res Lab Edwards AFB CA USA

High-performance computing (HPC) has revolutionized our ability to perform detailed simulations of complex real-world processes. A prominent contemporary example is from aerospace propulsion, where HPC is used for rotating detonation rocket engine (RDRE) simulations in support of the design of next-generation rocket engines;however, these simulations take millions of core hours even on powerful supercomputers, which makes them impractical for engineering tasks like design exploration and risk assessment. data-driven reduced-order models (ROMs) aim to address this limitation by constructing computationally cheap yet sufficiently accurate approximations that serve as surrogates for the high-fidelity model. This paper contributes a distributed memory algorithm that achieves fast and scalable construction of predictive physics-based ROMs trained from sparse datasets of extremely large state dimension. The algorithm learns structured physics-based ROMs that approximate the dynamical systems underlying those datasets. This enables model reduction for problems at a scale and complexity that exceeds the capabilities of standard, serial approaches. We demonstrate our algorithm's scalability using up to 2,048 cores on the Frontera supercomputer at the Texas Advanced Computing Center. We focus on a real-world three-dimensional RDRE for which one millisecond of simulated physical time requires one million core hours on a supercomputer. Using a training dataset of 2,536 snapshots each of state dimension 76 million, our distributed algorithm enables the construction of a predictive data-driven reduced model in just 13 seconds on 2,048 cores on Frontera.

关键词： High-performance computing data-driven modeling Scientific machine learning Large-scale simulations Rocket combustion

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data-driven modeling and Prediction of Obstructive Sleep Apnea based on Physics-guided Pathophysiological Understanding

Data-driven Modeling and Prediction of Obstructive Sleep Apn...

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IEEE International Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE)

作者： Chukwuemeka Ochieze Jiacheng Guo Yuchen Gong Younghoon Kwon Haibo Dong Ye Sun Department of Mechanical and Aerospace Engineering University of Virginia Charlottesville VA Department of Cardiovascular Medicine University of Washington Seattle WA Department of Electrical and Computer Engineering University of Virginia Charlottesville VA

ISBN: (数字)9781450394765

Obstructive sleep apnea (OSA) is the most common sleep orientated breathing disorder. OSA is characterized by upper airway obstruction, which is highly body position dependent. Specifically, head and head-neck position are critical in determining airway collapse, and adjusting head and head-neck positioning can be useful therapy for OSA. Thus, a real time monitoring and intervention of the head neck position and the corresponding airway collapsibility during sleep would allow an individualized adaptive positioning therapy. The main challenges of developing real-time OSA treatment systems include the unclear pathophysiological understanding of the collapse of pharyngeal walls and thus real-time cyber-physical system (CPS) based OSA therapy is underdeveloped. In this paper, we establish a data-driven method for modelling and predicting the occurrence of OSA in real time based on physics-guided pathophysiological understanding, which provides the basis for real-time CPS-based OSA therapy towards personalized and automated treatment.

关键词： data-driven modeling Adaptation models Medical treatment Predictive models Cyber-physical systems Sleep apnea Real-time systems

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data-driven modeling and Control for Lane Keeping System of Automated Driving Vehicles: Koopman Operator Approach

Data-Driven Modeling and Control for Lane Keeping System of ...

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International Conference on Control, Automation and Systems ( ICCAS)

作者： Jin Sung Kim Ying Shuai Quan Chung Choo Chung Department of Electrical Engineering Hanyang University Seoul Republic of Korea Division of Electrical and Biomedical Engineering Hanyang University Seoul Republic of Korea

This paper proposes the data-driven modeling and control method with the Koopman operator for the lane-keeping system. The vehicle can be modeled as a linear motion model but has underlying complicated nonlinear behavior. Thus, there exists a need to model the full vehicle dynamics effectively. To this end, we use the Koopman operator to express the full vehicle nonlinear dynamics as a linear structure. However, it is not practical to use the Koopman operator directly because it lies in infinite-dimensional space. Hence, we apply the extended dynamic mode decomposition to approximate the Koopman operator as a finite-dimensional linear operator. We conduct a comparative study between the linear model-based optimal control and the Koopman operator-based optimal control. As a result, it is observed that the proposed method reduces the system state by 20% compared to the linear model-based controller.

关键词： data-driven modeling Automation Force Optimal control Aerospace electronics Tires Nonlinear dynamical systems

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Machine learning-based data-driven modeling of deep-sea mining vehicles

Machine learning-based data-driven modeling of deep-sea mini...

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Computer Applications and Information Technology (ISCAIT), International Symposium on

作者： Xiaosong Liu Hao Zheng Saifeng Peng Changsha Research Institute of Mining and Metallurgy Co Ltd Changsha China

ISBN: (数字)9798331542856

ISBN: (纸本)9798331542863

This study proposes a data-driven modeling method for mining vehicle motion that aims to address the limitations of traditional methods and the challenges of adaptability to real-world environments. The proposed approach utilizes the APSO-XGBoost enhanced fusion algorithm, training a data-driven model with experimental data from the RecurDyn simulation. The model is optimized using data enhancement techniques, highlighting the differential speed data features and integrating the APSO algorithm. A comparison of the proposed method with traditional approaches reveals its superiority in predicting the speed and angular velocity of the mining vehicle. This provides a novel foundation for the intelligent control of deep-sea mining vehicles.

关键词： data-driven modeling Training Adaptation models Accuracy Computational modeling Kinematics Predictive models Prediction algorithms data mining Optimization

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ODEs Learn to Walk: ODE-Net based data-driven modeling for Crowd Dynamics 24

ODEs Learn to Walk: ODE-Net based Data-Driven Modeling for C...

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Proceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems

作者： Chen Cheng Jinglai Li Shanghai Jiao Tong University Shanghai China University of Birmingham Edgbaston United Kingdom

ISBN: (纸本)9798400704864

Predicting the behaviors of pedestrian crowds is of critical importance for a variety of real-world problems. data driven modeling, which aims to learn the mathematical models from observed data, is a promising tool to construct models that can make accurate predictions of such systems. In this work, we present a data-driven modeling approach based on the ODE-Net framework, for constructing continuous-time models of crowd dynamics. We discuss some challenging issues in applying the ODE-Net method to such problems, which are primarily associated with the dimensionality of the underlying crowd system, and we propose to address these issues by incorporating the social-force concept in the ODE-Net framework. Finally application examples are provided to demonstrate the performance of the proposed method.

关键词： crowd dynamics data-driven modeling ode-net social force

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Shape Estimation of Continuum Arms Based on Inverse Kinematics data-driven modeling

Shape Estimation of Continuum Arms Based on Inverse Kinemati...

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RSI/ISM International Conference on Robotics and Mechatronics (ICROM)

作者： Saeedeh Shekari S. Ali A. Moosavian Dept of Mechanical Engineering Advanced Robotics and Automated Systems (ARAS) Lab. Center of Excellence in Robotics and Control K. N. Toosi University of Technology Tehran Iran

ISBN: (数字)9798331529734

ISBN: (纸本)9798331529741

In recent decades, soft robotic arms inspired by biological organisms with flexible structures have enabled significant advancements in robotics, particularly by allowing robots to navigate in complex and unknown environments. Tendon-driven continuum robotic arms, as a type of such systems, are characterized by their infinite degrees of freedom and the inherent complexity of their nonlinear systems, while the analysis of their configurations is highly challenging. In this study, a data-driven inverse kinematics model is developed to estimate the shape of a two-segment continuum robot called RoboArm in ARAS Lab. The robot's final configuration was recorded using two cameras, and the shape estimation was performed by tracking the position of each segment's end along a given dynamic path. The experimental results were compared with software simulations which demonstrate high accuracy in predicting the arm's configuration. By comparing the estimated shapes from the simulations with the robot's actual shape during movement, we validated the effectiveness of this approach for accurately modeling the robot's configuration in real-time.

关键词： data-driven modeling Accuracy Shape Tracking Estimation Kinematics Soft robotics Real-time systems Software Continuum robots

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data driven modeling advancements for air temperature predictions in data centers

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NUMERICAL HEAT TRANSFER PART B-FUNDAMENTALS 2025年第2期86卷 244-261页

作者： Patel, Dhaval Joshi, Yogendra Georgia Inst Technol George W Woodruff Sch Mech Engn Atlanta GA USA Georgia Inst Technol George W Woodruff Sch Mech Engn Atlanta GA 30332 USA

While data center cooling energy usage optimization studies have been performed through computational fluid dynamics/heat transfer (CFD/HT), and heuristic methods, data driven modeling techniques are now also being used for these applications. This paper investigates the air temperature prediction capabilities of static artificial neural network (ANN), Gaussian progress regression (GPR), support vector regression (SVR), relevance vector machine (RVM), linear regression, and regression trees;and transient long-short term memory (LSTM), and nonlinear autoregressive neural network with external input (NARX)) data driven modeling frameworks. The static study compared various models and found that GPR provided the best results (average error of 0.56 degrees C), closely followed by the ANN and SVR (average error of 0.60 degrees C and 0.68 degrees C respectively) methods. The transient study compared models based on an experimental data set and found that NARX outperforms LSTM for normal operations (0.83 degrees C and 1.07 degrees C average error respectively), and that data driven models are able to provide relatively good predictions, even if the input variables are slightly outside the training domain.

关键词： data center data-driven modeling LSTM NARX thermal modeling

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data-driven modeling to predict the load vs. displacement curves of targeted composite materials for industry 4.0 and smart manufacturing

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COMPOSITE STRUCTURES 2021年 258卷 113207-113207页

作者： Kazi, Monzure-Khoda Eljack, Fadwa Mahdi, E. Qatar Univ Dept Chem Engn Coll Engn Doha 2713 Qatar Qatar Univ Dept Mech & Ind Engn Coll Engn Doha 2713 Qatar

This work presents an approach for smart manufacturing focusing on Industry 4.0 to predict the load vs. displacement curve of targeted cotton fiber/Polypropylene (PP) composite materials while complying with the required intended properties. Experimental data for varying composite fiber percentage to characteristic load and earlier built artificial neural network (ANN) models are used as the feed. A newly built ANN model is being trained and tested on the TensorFlow backend using the Keras library in Python to predict the load vs. displacement curves for any in-between values of the experimental range (e.g., 0-50% cotton fiber filler content in PP) without doing any further experiment. Finally, a Python package for the sparse identification of nonlinear dynamical (PySINDy) systems is used to identify the exact data-driven ANN model through the system identification, which will facilitate the effective implementation of the control algorithms, smart internet of things (IoT), and high-tech automated system.

关键词： Machine learning Artificial neural network data-driven modeling Industry 4.0 Smart manufacturing Fiber-reinforced polymer

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